There has been proposed a technique of correcting a discrepancy between a forecast and an actuality experientially through machine learning. This technique has a problem that an operation plan cannot be correctly made in the case where a significant discrepancy between a forecast result and actual data occurs in an unexpected fashion. In addition, this technique is based on the premise that data used for forecasting can be collected with a relatively high frequency. Hence, this technique has a problem that only a plan with a low reliability level can be made in the case where the collection interval of the data is long and thereby the reliability of the forecast thus decreases.
There has also been proposed, as another conventional technique, a technique in which: a plurality of demander nodes are kept informed of an amount of supply-demand unbalance that is sequentially observed; and the unbalance is solved by decentralized cooperative processing of the plurality of demander nodes. This technique is based on the premise that the plurality of demanders can sequentially observe the amount of supply-demand unbalance, but an amount of information that the plurality of demanders can access at the same time is limited due to a network capacity. In addition, such a method that can control the access does not exist.